Overview

Dataset statistics

Number of variables29
Number of observations50
Missing cells29
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 KiB
Average record size in memory234.6 B

Variable types

Numeric9
Categorical20

Alerts

airdate has constant value "2020-12-27" Constant
_embedded_show_dvdCountry has constant value "nan" Constant
season is highly correlated with numberHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with number and 2 other fieldsHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_webChannel and 13 other fieldsHigh correlation
_embedded_show_webChannel is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
summary is highly correlated with url and 3 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_webChannel and 9 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_summary and 9 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
name is highly correlated with _embedded_show_officialSite and 5 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_webChannel and 5 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_webChannel and 5 other fieldsHigh correlation
id is highly correlated with url and 19 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 22 other fieldsHigh correlation
season is highly correlated with id and 14 other fieldsHigh correlation
number is highly correlated with id and 15 other fieldsHigh correlation
type is highly correlated with id and 10 other fieldsHigh correlation
airtime is highly correlated with url and 13 other fieldsHigh correlation
airstamp is highly correlated with id and 21 other fieldsHigh correlation
runtime is highly correlated with id and 19 other fieldsHigh correlation
summary is highly correlated with url and 4 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 19 other fieldsHigh correlation
_embedded_show_genres is highly correlated with url and 20 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_webChannel is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 18 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
number has 2 (4.0%) missing values Missing
runtime has 3 (6.0%) missing values Missing
_embedded_show_runtime has 18 (36.0%) missing values Missing
_embedded_show_averageRuntime has 3 (6.0%) missing values Missing
_embedded_show_webChannel has 3 (6.0%) missing values Missing
url is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:21:58.441066
Analysis finished2022-05-10 02:22:17.571336
Duration19.13 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2031250.82
Minimum1953071
Maximum2318113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:17.639709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1953071
5-th percentile1972197.65
Q11988853.5
median1993651.5
Q32014606
95-th percentile2246403.25
Maximum2318113
Range365042
Interquartile range (IQR)25752.5

Descriptive statistics

Standard deviation89063.92661
Coefficient of variation (CV)0.04384683848
Kurtosis2.722213579
Mean2031250.82
Median Absolute Deviation (MAD)9812
Skewness1.95324965
Sum101562541
Variance7932383023
MonotonicityNot monotonic
2022-05-09T21:22:17.774596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19934961
 
2.0%
20343601
 
2.0%
19936511
 
2.0%
19936521
 
2.0%
19936531
 
2.0%
19936541
 
2.0%
19947101
 
2.0%
19954911
 
2.0%
19975321
 
2.0%
19975331
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
19530711
2.0%
19563411
2.0%
19706781
2.0%
19740551
2.0%
19740561
2.0%
19751891
2.0%
19751901
2.0%
19757471
2.0%
19780141
2.0%
19816021
2.0%
ValueCountFrequency (%)
23181131
2.0%
22673181
2.0%
22559861
2.0%
22346911
2.0%
22044511
2.0%
21761441
2.0%
21659321
2.0%
21262281
2.0%
21117611
2.0%
20525121
2.0%

url
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh
 
1
https://www.tvmaze.com/episodes/2034360/lulu-1x01-episode-1
 
1
https://www.tvmaze.com/episodes/1993651/outlier-1x05-episode-5
 
1
https://www.tvmaze.com/episodes/1993652/outlier-1x06-episode-6
 
1
https://www.tvmaze.com/episodes/1993653/outlier-1x07-episode-7
 
1
Other values (45)
45 

Length

Max length139
Median length102
Mean length77.8
Min length59

Characters and Unicode

Total characters3890
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh
2nd rowhttps://www.tvmaze.com/episodes/1993442/top-10-po-versii-seasonvarru-2x12-top-10-samyh-ozidaemyh-novinok-v-mire-serialov
3rd rowhttps://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-12
4th rowhttps://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-6
5th rowhttps://www.tvmaze.com/episodes/2052512/wu-shen-zhu-zai-1x87-episode-87

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh1
 
2.0%
https://www.tvmaze.com/episodes/2034360/lulu-1x01-episode-11
 
2.0%
https://www.tvmaze.com/episodes/1993651/outlier-1x05-episode-51
 
2.0%
https://www.tvmaze.com/episodes/1993652/outlier-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/1993653/outlier-1x07-episode-71
 
2.0%
https://www.tvmaze.com/episodes/1993654/outlier-1x08-episode-81
 
2.0%
https://www.tvmaze.com/episodes/1994710/the-controllers-1x05-episode-51
 
2.0%
https://www.tvmaze.com/episodes/1995491/the-controllers-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/1997532/the-penalty-zone-1x25-episode-251
 
2.0%
https://www.tvmaze.com/episodes/1997533/the-penalty-zone-1x26-episode-261
 
2.0%
Other values (40)40
80.0%

Length

2022-05-09T21:22:17.909331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh1
 
2.0%
https://www.tvmaze.com/episodes/1978014/30-monedas-1x05-el-doble1
 
2.0%
https://www.tvmaze.com/episodes/1993647/outlier-1x01-episode-11
 
2.0%
https://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-121
 
2.0%
https://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/2052512/wu-shen-zhu-zai-1x87-episode-871
 
2.0%
https://www.tvmaze.com/episodes/2012323/mans-diary-2x08-episode-81
 
2.0%
https://www.tvmaze.com/episodes/2005757/legend-of-yun-qian-1x10-episode-101
 
2.0%
https://www.tvmaze.com/episodes/1974055/love-revolution-1x29-episode-291
 
2.0%
https://www.tvmaze.com/episodes/1974056/love-revolution-1x30-episode-301
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
e333
 
8.6%
-280
 
7.2%
s254
 
6.5%
/250
 
6.4%
t232
 
6.0%
o220
 
5.7%
i163
 
4.2%
w161
 
4.1%
p155
 
4.0%
a154
 
4.0%
Other values (30)1688
43.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2636
67.8%
Decimal Number574
 
14.8%
Other Punctuation400
 
10.3%
Dash Punctuation280
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e333
12.6%
s254
 
9.6%
t232
 
8.8%
o220
 
8.3%
i163
 
6.2%
w161
 
6.1%
p155
 
5.9%
a154
 
5.8%
m126
 
4.8%
d109
 
4.1%
Other values (16)729
27.7%
Decimal Number
ValueCountFrequency (%)
1123
21.4%
276
13.2%
968
11.8%
068
11.8%
549
 
8.5%
646
 
8.0%
345
 
7.8%
737
 
6.4%
831
 
5.4%
431
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/250
62.5%
.100
 
25.0%
:50
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2636
67.8%
Common1254
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e333
12.6%
s254
 
9.6%
t232
 
8.8%
o220
 
8.3%
i163
 
6.2%
w161
 
6.1%
p155
 
5.9%
a154
 
5.8%
m126
 
4.8%
d109
 
4.1%
Other values (16)729
27.7%
Common
ValueCountFrequency (%)
-280
22.3%
/250
19.9%
1123
9.8%
.100
 
8.0%
276
 
6.1%
968
 
5.4%
068
 
5.4%
:50
 
4.0%
549
 
3.9%
646
 
3.7%
Other values (4)144
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e333
 
8.6%
-280
 
7.2%
s254
 
6.5%
/250
 
6.4%
t232
 
6.0%
o220
 
5.7%
i163
 
4.2%
w161
 
4.1%
p155
 
4.0%
a154
 
4.0%
Other values (30)1688
43.4%

name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Episode 5
Episode 6
Episode 1
Episode 8
 
2
Episode 3
 
2
Other values (34)
34 

Length

Max length82
Median length61
Mean length18.52
Min length6

Characters and Unicode

Total characters926
Distinct characters106
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)68.0%

Sample

1st rowБобровый мех
2nd rowТОП-10 самых ожидаемых новинок в мире сериалов
3rd rowEpisode 12
4th rowEpisode 6
5th rowEpisode 87

Common Values

ValueCountFrequency (%)
Episode 54
 
8.0%
Episode 64
 
8.0%
Episode 14
 
8.0%
Episode 82
 
4.0%
Episode 32
 
4.0%
Бобровый мех1
 
2.0%
Постдеконструкция с Владимиром Сурдиным. Фильм "Интерстеллар"1
 
2.0%
Episode 41
 
2.0%
Episode 71
 
2.0%
Episode 251
 
2.0%
Other values (29)29
58.0%

Length

2022-05-09T21:22:18.042673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode27
 
16.6%
66
 
3.7%
54
 
2.5%
14
 
2.5%
the3
 
1.8%
of2
 
1.2%
72
 
1.2%
de2
 
1.2%
32
 
1.2%
82
 
1.2%
Other values (109)109
66.9%

Most occurring characters

ValueCountFrequency (%)
113
 
12.2%
e62
 
6.7%
s51
 
5.5%
i50
 
5.4%
o42
 
4.5%
d39
 
4.2%
a33
 
3.6%
E32
 
3.5%
p29
 
3.1%
n22
 
2.4%
Other values (96)453
48.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter631
68.1%
Space Separator113
 
12.2%
Uppercase Letter103
 
11.1%
Decimal Number55
 
5.9%
Other Punctuation22
 
2.4%
Dash Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e62
 
9.8%
s51
 
8.1%
i50
 
7.9%
o42
 
6.7%
d39
 
6.2%
a33
 
5.2%
p29
 
4.6%
n22
 
3.5%
о21
 
3.3%
r19
 
3.0%
Other values (42)263
41.7%
Uppercase Letter
ValueCountFrequency (%)
E32
31.1%
T8
 
7.8%
N5
 
4.9%
C5
 
4.9%
O5
 
4.9%
R4
 
3.9%
A3
 
2.9%
M3
 
2.9%
G3
 
2.9%
U2
 
1.9%
Other values (24)33
32.0%
Decimal Number
ValueCountFrequency (%)
212
21.8%
68
14.5%
18
14.5%
07
12.7%
56
10.9%
75
9.1%
34
 
7.3%
83
 
5.5%
41
 
1.8%
91
 
1.8%
Other Punctuation
ValueCountFrequency (%)
"6
27.3%
,5
22.7%
'3
13.6%
:2
 
9.1%
/2
 
9.1%
.2
 
9.1%
!1
 
4.5%
#1
 
4.5%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin522
56.4%
Cyrillic212
22.9%
Common192
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e62
11.9%
s51
 
9.8%
i50
 
9.6%
o42
 
8.0%
d39
 
7.5%
a33
 
6.3%
E32
 
6.1%
p29
 
5.6%
n22
 
4.2%
r19
 
3.6%
Other values (39)143
27.4%
Cyrillic
ValueCountFrequency (%)
о21
 
9.9%
е17
 
8.0%
а16
 
7.5%
р15
 
7.1%
и13
 
6.1%
с11
 
5.2%
н11
 
5.2%
в10
 
4.7%
м10
 
4.7%
т10
 
4.7%
Other values (27)78
36.8%
Common
ValueCountFrequency (%)
113
58.9%
212
 
6.2%
68
 
4.2%
18
 
4.2%
07
 
3.6%
"6
 
3.1%
56
 
3.1%
,5
 
2.6%
75
 
2.6%
34
 
2.1%
Other values (10)18
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII711
76.8%
Cyrillic212
 
22.9%
None3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
15.9%
e62
 
8.7%
s51
 
7.2%
i50
 
7.0%
o42
 
5.9%
d39
 
5.5%
a33
 
4.6%
E32
 
4.5%
p29
 
4.1%
n22
 
3.1%
Other values (56)238
33.5%
Cyrillic
ValueCountFrequency (%)
о21
 
9.9%
е17
 
8.0%
а16
 
7.5%
р15
 
7.1%
и13
 
6.1%
с11
 
5.2%
н11
 
5.2%
в10
 
4.7%
м10
 
4.7%
т10
 
4.7%
Other values (27)78
36.8%
None
ValueCountFrequency (%)
ă1
33.3%
é1
33.3%
ä1
33.3%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.48
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:18.130403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile1132.6
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation484.0576471
Coefficient of variation (CV)3.920129957
Kurtosis13.11824218
Mean123.48
Median Absolute Deviation (MAD)0
Skewness3.819857605
Sum6174
Variance234311.8057
MonotonicityNot monotonic
2022-05-09T21:22:18.219607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
134
68.0%
29
 
18.0%
20203
 
6.0%
61
 
2.0%
51
 
2.0%
31
 
2.0%
481
 
2.0%
ValueCountFrequency (%)
134
68.0%
29
 
18.0%
31
 
2.0%
51
 
2.0%
61
 
2.0%
481
 
2.0%
20203
 
6.0%
ValueCountFrequency (%)
20203
 
6.0%
481
 
2.0%
61
 
2.0%
51
 
2.0%
31
 
2.0%
29
 
18.0%
134
68.0%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)52.1%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean22.97916667
Minimum1
Maximum354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:18.313554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median7
Q325.25
95-th percentile55.25
Maximum354
Range353
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation52.23575428
Coefficient of variation (CV)2.27317879
Kurtosis35.86242737
Mean22.97916667
Median Absolute Deviation (MAD)4
Skewness5.680323477
Sum1103
Variance2728.574025
MonotonicityNot monotonic
2022-05-09T21:22:18.403629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
68
16.0%
56
 
12.0%
15
 
10.0%
83
 
6.0%
112
 
4.0%
122
 
4.0%
32
 
4.0%
72
 
4.0%
522
 
4.0%
41
 
2.0%
Other values (15)15
30.0%
(Missing)2
 
4.0%
ValueCountFrequency (%)
15
10.0%
21
 
2.0%
32
 
4.0%
41
 
2.0%
56
12.0%
68
16.0%
72
 
4.0%
83
 
6.0%
101
 
2.0%
112
 
4.0%
ValueCountFrequency (%)
3541
2.0%
871
2.0%
571
2.0%
522
4.0%
491
2.0%
411
2.0%
381
2.0%
371
2.0%
301
2.0%
291
2.0%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
regular
48 
insignificant_special
 
1
significant_special
 
1

Length

Max length21
Median length7
Mean length7.52
Min length7

Characters and Unicode

Total characters376
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular48
96.0%
insignificant_special1
 
2.0%
significant_special1
 
2.0%

Length

2022-05-09T21:22:18.497646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:18.592512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular48
96.0%
insignificant_special1
 
2.0%
significant_special1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r96
25.5%
a52
13.8%
e50
13.3%
g50
13.3%
l50
13.3%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (4)8
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter374
99.5%
Connector Punctuation2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r96
25.7%
a52
13.9%
e50
13.4%
g50
13.4%
l50
13.4%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (3)6
 
1.6%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin374
99.5%
Common2
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r96
25.7%
a52
13.9%
e50
13.4%
g50
13.4%
l50
13.4%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (3)6
 
1.6%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r96
25.5%
a52
13.8%
e50
13.3%
g50
13.3%
l50
13.3%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (4)8
 
2.1%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27
50 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-27
2nd row2020-12-27
3rd row2020-12-27
4th row2020-12-27
5th row2020-12-27

Common Values

ValueCountFrequency (%)
2020-12-2750
100.0%

Length

2022-05-09T21:22:18.666609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:18.745239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2750
100.0%

Most occurring characters

ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
80.0%
Dash Punctuation100
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2200
50.0%
0100
25.0%
150
 
12.5%
750
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
nan
37 
20:00
10:00
 
3
17:00
 
2
18:00
 
1
Other values (2)
 
2

Length

Max length5
Median length3
Mean length3.52
Min length3

Characters and Unicode

Total characters176
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.0%

Sample

1st rownan
2nd rownan
3rd row10:00
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
nan37
74.0%
20:005
 
10.0%
10:003
 
6.0%
17:002
 
4.0%
18:001
 
2.0%
12:001
 
2.0%
12:151
 
2.0%

Length

2022-05-09T21:22:18.824071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:18.933543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan37
74.0%
20:005
 
10.0%
10:003
 
6.0%
17:002
 
4.0%
18:001
 
2.0%
12:001
 
2.0%
12:151
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n74
42.0%
a37
21.0%
032
18.2%
:13
 
7.4%
19
 
5.1%
27
 
4.0%
72
 
1.1%
81
 
0.6%
51
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111
63.1%
Decimal Number52
29.5%
Other Punctuation13
 
7.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032
61.5%
19
 
17.3%
27
 
13.5%
72
 
3.8%
81
 
1.9%
51
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
n74
66.7%
a37
33.3%
Other Punctuation
ValueCountFrequency (%)
:13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111
63.1%
Common65
36.9%

Most frequent character per script

Common
ValueCountFrequency (%)
032
49.2%
:13
20.0%
19
 
13.8%
27
 
10.8%
72
 
3.1%
81
 
1.5%
51
 
1.5%
Latin
ValueCountFrequency (%)
n74
66.7%
a37
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n74
42.0%
a37
21.0%
032
18.2%
:13
 
7.4%
19
 
5.1%
27
 
4.0%
72
 
1.1%
81
 
0.6%
51
 
0.6%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27T12:00:00+00:00
27 
2020-12-27T11:00:00+00:00
2020-12-27T17:00:00+00:00
2020-12-27T02:00:00+00:00
2020-12-27T00:00:00+00:00
 
2
Other values (6)

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1250
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.0%

Sample

1st row2020-12-27T00:00:00+00:00
2nd row2020-12-27T00:00:00+00:00
3rd row2020-12-27T02:00:00+00:00
4th row2020-12-27T02:00:00+00:00
5th row2020-12-27T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-27T12:00:00+00:0027
54.0%
2020-12-27T11:00:00+00:005
 
10.0%
2020-12-27T17:00:00+00:005
 
10.0%
2020-12-27T02:00:00+00:003
 
6.0%
2020-12-27T00:00:00+00:002
 
4.0%
2020-12-27T04:00:00+00:002
 
4.0%
2020-12-27T08:00:00+00:002
 
4.0%
2020-12-27T09:00:00+00:001
 
2.0%
2020-12-27T14:00:00+00:001
 
2.0%
2020-12-27T17:15:00+00:001
 
2.0%

Length

2022-05-09T21:22:19.028713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-27t12:00:00+00:0027
54.0%
2020-12-27t11:00:00+00:005
 
10.0%
2020-12-27t17:00:00+00:005
 
10.0%
2020-12-27t02:00:00+00:003
 
6.0%
2020-12-27t00:00:00+00:002
 
4.0%
2020-12-27t04:00:00+00:002
 
4.0%
2020-12-27t08:00:00+00:002
 
4.0%
2020-12-27t09:00:00+00:001
 
2.0%
2020-12-27t14:00:00+00:001
 
2.0%
2020-12-27t17:15:00+00:001
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0511
40.9%
2230
18.4%
:150
 
12.0%
-100
 
8.0%
196
 
7.7%
755
 
4.4%
T50
 
4.0%
+50
 
4.0%
43
 
0.2%
83
 
0.2%
Other values (2)2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number900
72.0%
Other Punctuation150
 
12.0%
Dash Punctuation100
 
8.0%
Uppercase Letter50
 
4.0%
Math Symbol50
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0511
56.8%
2230
25.6%
196
 
10.7%
755
 
6.1%
43
 
0.3%
83
 
0.3%
91
 
0.1%
51
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:150
100.0%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%
Uppercase Letter
ValueCountFrequency (%)
T50
100.0%
Math Symbol
ValueCountFrequency (%)
+50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1200
96.0%
Latin50
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0511
42.6%
2230
19.2%
:150
 
12.5%
-100
 
8.3%
196
 
8.0%
755
 
4.6%
+50
 
4.2%
43
 
0.2%
83
 
0.2%
91
 
0.1%
Latin
ValueCountFrequency (%)
T50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0511
40.9%
2230
18.4%
:150
 
12.0%
-100
 
8.0%
196
 
7.7%
755
 
4.4%
T50
 
4.0%
+50
 
4.0%
43
 
0.2%
83
 
0.2%
Other values (2)2
 
0.2%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)51.1%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean36.57446809
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:19.126994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.9
Q120
median42
Q345
95-th percentile62.8
Maximum120
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.37012649
Coefficient of variation (CV)0.63897379
Kurtosis5.344035273
Mean36.57446809
Median Absolute Deviation (MAD)12
Skewness1.819461013
Sum1719
Variance546.1628122
MonotonicityNot monotonic
2022-05-09T21:22:19.209730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
459
18.0%
424
 
8.0%
153
 
6.0%
203
 
6.0%
433
 
6.0%
1202
 
4.0%
302
 
4.0%
122
 
4.0%
442
 
4.0%
252
 
4.0%
Other values (14)15
30.0%
(Missing)3
 
6.0%
ValueCountFrequency (%)
41
 
2.0%
71
 
2.0%
81
 
2.0%
111
 
2.0%
122
4.0%
153
6.0%
191
 
2.0%
203
6.0%
211
 
2.0%
222
4.0%
ValueCountFrequency (%)
1202
 
4.0%
641
 
2.0%
601
 
2.0%
591
 
2.0%
501
 
2.0%
459
18.0%
442
 
4.0%
433
 
6.0%
424
8.0%
401
 
2.0%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
nan
35 
<p>After mysteriously disappearing two years ago, Elena's husband miraculously returns. In Rome, Santoro tries to recruit Vergara, offering him unimaginable power…</p>
 
1
<p>A teenage girl, Sofie, disappears on her way home from a party in Kautokeino. A friend reports her missing to the police, but the case is not taken seriously. Not until Sofie is found murdered in a village a few hours away.</p>
 
1
<p>Maja defies all warnings and begins to investigate on her own accord. And she grows increasingly confident in her theory - the police are wrong.</p>
 
1
<p>Maja's theory of a possible serial killer is finally heard by the police, who permit her to join the investigation despite their doubts. She begins to dig into old cases in the archives, searching for a pattern.</p><p> </p>
 
1
Other values (11)
11 

Length

Max length234
Median length3
Mean length53.56
Min length3

Characters and Unicode

Total characters2678
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)30.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan35
70.0%
<p>After mysteriously disappearing two years ago, Elena's husband miraculously returns. In Rome, Santoro tries to recruit Vergara, offering him unimaginable power…</p>1
 
2.0%
<p>A teenage girl, Sofie, disappears on her way home from a party in Kautokeino. A friend reports her missing to the police, but the case is not taken seriously. Not until Sofie is found murdered in a village a few hours away.</p>1
 
2.0%
<p>Maja defies all warnings and begins to investigate on her own accord. And she grows increasingly confident in her theory - the police are wrong.</p>1
 
2.0%
<p>Maja's theory of a possible serial killer is finally heard by the police, who permit her to join the investigation despite their doubts. She begins to dig into old cases in the archives, searching for a pattern.</p><p> </p>1
 
2.0%
<p>Maja and the other investigators are looking for connections between several murders and missing person cases. They discover that the pattern much more extensive than first thought.</p><p> </p>1
 
2.0%
<p>Maja is reduced to the sidelines and is close to giving up. But information about Maja's own family sheds new light on the murder. Could the killer have been a part of Maja's inner circle</p><p> </p>1
 
2.0%
<p>Maja's confrontation with her father has major consequences. The day after their feud, he is found dead at home having taken his own life. The shock of the loss of her father sends Maja even closer to the tipping point.</p><p> </p>1
 
2.0%
<p>Despite all the warnings about dragging her private trauma into the case, Maja decides to try one last time to talk to her demented mother.</p><p> </p>1
 
2.0%
<p>After going down what turns out to be a blind alley, the suspicion falls on a man who was close to Maja's family in his youth. The police strike, but he escapes and disappears.</p><p> </p>1
 
2.0%
Other values (6)6
 
12.0%

Length

2022-05-09T21:22:19.319458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan35
 
7.5%
the26
 
5.6%
to17
 
3.7%
a14
 
3.0%
and9
 
1.9%
her9
 
1.9%
on8
 
1.7%
in8
 
1.7%
is8
 
1.7%
p7
 
1.5%
Other values (252)323
69.6%

Most occurring characters

ValueCountFrequency (%)
407
15.2%
e240
 
9.0%
n206
 
7.7%
a197
 
7.4%
t152
 
5.7%
i145
 
5.4%
o138
 
5.2%
r128
 
4.8%
s126
 
4.7%
h102
 
3.8%
Other values (52)837
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2031
75.8%
Space Separator414
 
15.5%
Math Symbol84
 
3.1%
Other Punctuation81
 
3.0%
Uppercase Letter61
 
2.3%
Decimal Number5
 
0.2%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e240
11.8%
n206
10.1%
a197
 
9.7%
t152
 
7.5%
i145
 
7.1%
o138
 
6.8%
r128
 
6.3%
s126
 
6.2%
h102
 
5.0%
p86
 
4.2%
Other values (15)511
25.2%
Uppercase Letter
ValueCountFrequency (%)
M12
19.7%
T8
13.1%
A5
8.2%
S5
8.2%
Y4
 
6.6%
W4
 
6.6%
N4
 
6.6%
I3
 
4.9%
E2
 
3.3%
R2
 
3.3%
Other values (9)12
19.7%
Other Punctuation
ValueCountFrequency (%)
.27
33.3%
/21
25.9%
,18
22.2%
'9
 
11.1%
"2
 
2.5%
:2
 
2.5%
1
 
1.2%
?1
 
1.2%
Decimal Number
ValueCountFrequency (%)
71
20.0%
61
20.0%
81
20.0%
91
20.0%
11
20.0%
Space Separator
ValueCountFrequency (%)
407
98.3%
 7
 
1.7%
Math Symbol
ValueCountFrequency (%)
>42
50.0%
<42
50.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2092
78.1%
Common586
 
21.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e240
11.5%
n206
 
9.8%
a197
 
9.4%
t152
 
7.3%
i145
 
6.9%
o138
 
6.6%
r128
 
6.1%
s126
 
6.0%
h102
 
4.9%
p86
 
4.1%
Other values (34)572
27.3%
Common
ValueCountFrequency (%)
407
69.5%
>42
 
7.2%
<42
 
7.2%
.27
 
4.6%
/21
 
3.6%
,18
 
3.1%
'9
 
1.5%
 7
 
1.2%
"2
 
0.3%
-2
 
0.3%
Other values (8)9
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2670
99.7%
None7
 
0.3%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
407
15.2%
e240
 
9.0%
n206
 
7.7%
a197
 
7.4%
t152
 
5.7%
i145
 
5.4%
o138
 
5.2%
r128
 
4.8%
s126
 
4.7%
h102
 
3.8%
Other values (50)829
31.0%
None
ValueCountFrequency (%)
 7
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46742.66
Minimum10892
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:19.507304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10892
5-th percentile18109.6
Q143157.5
median52424
Q352743
95-th percentile59702.15
Maximum61755
Range50863
Interquartile range (IQR)9585.5

Descriptive statistics

Standard deviation12416.46129
Coefficient of variation (CV)0.2656344609
Kurtosis1.839173152
Mean46742.66
Median Absolute Deviation (MAD)1427
Skewness-1.613574112
Sum2337133
Variance154168510.9
MonotonicityNot monotonic
2022-05-09T21:22:19.616940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
526538
 
16.0%
349402
 
4.0%
526852
 
4.0%
527432
 
4.0%
527812
 
4.0%
499482
 
4.0%
108921
 
2.0%
599511
 
2.0%
536691
 
2.0%
586451
 
2.0%
Other values (28)28
56.0%
ValueCountFrequency (%)
108921
2.0%
129061
2.0%
175841
2.0%
187521
2.0%
196281
2.0%
306061
2.0%
334631
2.0%
349402
4.0%
369071
2.0%
393141
2.0%
ValueCountFrequency (%)
617551
2.0%
602461
2.0%
599511
2.0%
593981
2.0%
586451
2.0%
540331
2.0%
536691
2.0%
534671
2.0%
530941
2.0%
528981
2.0%

_embedded_show_url
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://www.tvmaze.com/shows/52653/outlier
https://www.tvmaze.com/shows/34940/fancy-nancy
 
2
https://www.tvmaze.com/shows/52685/the-controllers
 
2
https://www.tvmaze.com/shows/52743/the-penalty-zone
 
2
https://www.tvmaze.com/shows/52781/love-script
 
2
Other values (33)
34 

Length

Max length77
Median length59
Mean length48.16
Min length39

Characters and Unicode

Total characters2408
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowhttps://www.tvmaze.com/shows/10892/troe-iz-prostokvasino
2nd rowhttps://www.tvmaze.com/shows/19628/top-10-po-versii-seasonvarru
3rd rowhttps://www.tvmaze.com/shows/51471/hero-return
4th rowhttps://www.tvmaze.com/shows/52178/swallowed-star
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52653/outlier8
 
16.0%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
4.0%
https://www.tvmaze.com/shows/52685/the-controllers2
 
4.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
4.0%
https://www.tvmaze.com/shows/52781/love-script2
 
4.0%
https://www.tvmaze.com/shows/49948/love-revolution2
 
4.0%
https://www.tvmaze.com/shows/10892/troe-iz-prostokvasino1
 
2.0%
https://www.tvmaze.com/shows/59951/awesomeness-tvs-next-influencer1
 
2.0%
https://www.tvmaze.com/shows/53669/lulu1
 
2.0%
https://www.tvmaze.com/shows/58645/the-motive1
 
2.0%
Other values (28)28
56.0%

Length

2022-05-09T21:22:19.726455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52653/outlier8
 
16.0%
https://www.tvmaze.com/shows/52685/the-controllers2
 
4.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
4.0%
https://www.tvmaze.com/shows/52781/love-script2
 
4.0%
https://www.tvmaze.com/shows/49948/love-revolution2
 
4.0%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
4.0%
https://www.tvmaze.com/shows/49524/30-monedas1
 
2.0%
https://www.tvmaze.com/shows/54033/wu-shen-zhu-zai1
 
2.0%
https://www.tvmaze.com/shows/50398/mans-diary1
 
2.0%
https://www.tvmaze.com/shows/52898/legend-of-yun-qian1
 
2.0%
Other values (28)28
56.0%

Most occurring characters

ValueCountFrequency (%)
/250
 
10.4%
w206
 
8.6%
t196
 
8.1%
s185
 
7.7%
o151
 
6.3%
e119
 
4.9%
h117
 
4.9%
m108
 
4.5%
.100
 
4.2%
a94
 
3.9%
Other values (30)882
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1694
70.3%
Other Punctuation400
 
16.6%
Decimal Number255
 
10.6%
Dash Punctuation59
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w206
12.2%
t196
11.6%
s185
10.9%
o151
8.9%
e119
 
7.0%
h117
 
6.9%
m108
 
6.4%
a94
 
5.5%
c75
 
4.4%
p67
 
4.0%
Other values (16)376
22.2%
Decimal Number
ValueCountFrequency (%)
550
19.6%
333
12.9%
228
11.0%
428
11.0%
925
9.8%
624
9.4%
120
 
7.8%
819
 
7.5%
015
 
5.9%
713
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/250
62.5%
.100
 
25.0%
:50
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1694
70.3%
Common714
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w206
12.2%
t196
11.6%
s185
10.9%
o151
8.9%
e119
 
7.0%
h117
 
6.9%
m108
 
6.4%
a94
 
5.5%
c75
 
4.4%
p67
 
4.0%
Other values (16)376
22.2%
Common
ValueCountFrequency (%)
/250
35.0%
.100
 
14.0%
-59
 
8.3%
:50
 
7.0%
550
 
7.0%
333
 
4.6%
228
 
3.9%
428
 
3.9%
925
 
3.5%
624
 
3.4%
Other values (4)67
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/250
 
10.4%
w206
 
8.6%
t196
 
8.1%
s185
 
7.7%
o151
 
6.3%
e119
 
4.9%
h117
 
4.9%
m108
 
4.5%
.100
 
4.2%
a94
 
3.9%
Other values (30)882
36.6%

_embedded_show_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Outlier
Fancy Nancy
 
2
The Controllers
 
2
The Penalty Zone
 
2
Love Script
 
2
Other values (33)
34 

Length

Max length43
Median length21
Mean length13.4
Min length5

Characters and Unicode

Total characters670
Distinct characters88
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowТрое из Простоквашино
2nd rowТОП-10 по версии Seasonvar.ru
3rd rowHero Return
4th rowSwallowed Star
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
Outlier8
 
16.0%
Fancy Nancy2
 
4.0%
The Controllers2
 
4.0%
The Penalty Zone2
 
4.0%
Love Script2
 
4.0%
Love Revolution2
 
4.0%
Трое из Простоквашино1
 
2.0%
Awesomeness TV's Next Influencer1
 
2.0%
Lu'lu'1
 
2.0%
The Motive1
 
2.0%
Other values (28)28
56.0%

Length

2022-05-09T21:22:19.835868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
outlier8
 
7.4%
the6
 
5.6%
love4
 
3.7%
zone2
 
1.9%
fancy2
 
1.9%
script2
 
1.9%
revolution2
 
1.9%
penalty2
 
1.9%
controllers2
 
1.9%
nancy2
 
1.9%
Other values (75)76
70.4%

Most occurring characters

ValueCountFrequency (%)
e63
 
9.4%
58
 
8.7%
n39
 
5.8%
i34
 
5.1%
a34
 
5.1%
r33
 
4.9%
o33
 
4.9%
t32
 
4.8%
l29
 
4.3%
u27
 
4.0%
Other values (78)288
43.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter494
73.7%
Uppercase Letter101
 
15.1%
Space Separator58
 
8.7%
Other Punctuation9
 
1.3%
Decimal Number5
 
0.7%
Open Punctuation1
 
0.1%
Dash Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e63
12.8%
n39
 
7.9%
i34
 
6.9%
a34
 
6.9%
r33
 
6.7%
o33
 
6.7%
t32
 
6.5%
l29
 
5.9%
u27
 
5.5%
s21
 
4.3%
Other values (38)149
30.2%
Uppercase Letter
ValueCountFrequency (%)
O11
 
10.9%
T10
 
9.9%
S9
 
8.9%
L9
 
8.9%
F6
 
5.9%
M5
 
5.0%
P5
 
5.0%
C5
 
5.0%
B4
 
4.0%
Z4
 
4.0%
Other values (19)33
32.7%
Decimal Number
ValueCountFrequency (%)
02
40.0%
31
20.0%
11
20.0%
71
20.0%
Other Punctuation
ValueCountFrequency (%)
'6
66.7%
:2
 
22.2%
.1
 
11.1%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin533
79.6%
Common75
 
11.2%
Cyrillic62
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e63
 
11.8%
n39
 
7.3%
i34
 
6.4%
a34
 
6.4%
r33
 
6.2%
o33
 
6.2%
t32
 
6.0%
l29
 
5.4%
u27
 
5.1%
s21
 
3.9%
Other values (41)188
35.3%
Cyrillic
ValueCountFrequency (%)
и8
 
12.9%
о6
 
9.7%
е5
 
8.1%
к4
 
6.5%
р4
 
6.5%
с4
 
6.5%
а3
 
4.8%
я2
 
3.2%
Т2
 
3.2%
п2
 
3.2%
Other values (16)22
35.5%
Common
ValueCountFrequency (%)
58
77.3%
'6
 
8.0%
02
 
2.7%
:2
 
2.7%
31
 
1.3%
(1
 
1.3%
.1
 
1.3%
11
 
1.3%
-1
 
1.3%
71
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII604
90.1%
Cyrillic62
 
9.3%
None4
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e63
 
10.4%
58
 
9.6%
n39
 
6.5%
i34
 
5.6%
a34
 
5.6%
r33
 
5.5%
o33
 
5.5%
t32
 
5.3%
l29
 
4.8%
u27
 
4.5%
Other values (50)222
36.8%
Cyrillic
ValueCountFrequency (%)
и8
 
12.9%
о6
 
9.7%
е5
 
8.1%
к4
 
6.5%
р4
 
6.5%
с4
 
6.5%
а3
 
4.8%
я2
 
3.2%
Т2
 
3.2%
п2
 
3.2%
Other values (16)22
35.5%
None
ValueCountFrequency (%)
í2
50.0%
á2
50.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Scripted
29 
Animation
Talk Show
Documentary
Reality
 
2
Other values (2)

Length

Max length11
Median length8
Mean length8.26
Min length4

Characters and Unicode

Total characters413
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowAnimation
2nd rowTalk Show
3rd rowAnimation
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted29
58.0%
Animation8
 
16.0%
Talk Show5
 
10.0%
Documentary3
 
6.0%
Reality2
 
4.0%
News2
 
4.0%
Game Show1
 
2.0%

Length

2022-05-09T21:22:19.929889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:20.039499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted29
51.8%
animation8
 
14.3%
show6
 
10.7%
talk5
 
8.9%
documentary3
 
5.4%
reality2
 
3.6%
news2
 
3.6%
game1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i47
11.4%
t42
10.2%
e37
9.0%
S35
 
8.5%
r32
 
7.7%
c32
 
7.7%
p29
 
7.0%
d29
 
7.0%
a19
 
4.6%
n19
 
4.6%
Other values (16)92
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter351
85.0%
Uppercase Letter56
 
13.6%
Space Separator6
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i47
13.4%
t42
12.0%
e37
10.5%
r32
9.1%
c32
9.1%
p29
8.3%
d29
8.3%
a19
 
5.4%
n19
 
5.4%
o17
 
4.8%
Other values (8)48
13.7%
Uppercase Letter
ValueCountFrequency (%)
S35
62.5%
A8
 
14.3%
T5
 
8.9%
D3
 
5.4%
R2
 
3.6%
N2
 
3.6%
G1
 
1.8%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin407
98.5%
Common6
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i47
11.5%
t42
10.3%
e37
9.1%
S35
8.6%
r32
 
7.9%
c32
 
7.9%
p29
 
7.1%
d29
 
7.1%
a19
 
4.7%
n19
 
4.7%
Other values (15)86
21.1%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i47
11.4%
t42
10.2%
e37
9.0%
S35
 
8.5%
r32
 
7.7%
c32
 
7.7%
p29
 
7.0%
d29
 
7.0%
a19
 
4.6%
n19
 
4.6%
Other values (16)92
22.3%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Chinese
11 
Norwegian
10 
English
Russian
Korean
Other values (9)
13 

Length

Max length10
Median length7
Mean length7.38
Min length6

Characters and Unicode

Total characters369
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)12.0%

Sample

1st rowRussian
2nd rowRussian
3rd rowChinese
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese11
22.0%
Norwegian10
20.0%
English8
16.0%
Russian5
10.0%
Korean3
 
6.0%
Spanish3
 
6.0%
Japanese2
 
4.0%
Arabic2
 
4.0%
Danish1
 
2.0%
Romanian1
 
2.0%
Other values (4)4
 
8.0%

Length

2022-05-09T21:22:20.133474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese11
22.0%
norwegian10
20.0%
english8
16.0%
russian5
10.0%
korean3
 
6.0%
spanish3
 
6.0%
japanese2
 
4.0%
arabic2
 
4.0%
danish1
 
2.0%
romanian1
 
2.0%
Other values (4)4
 
8.0%

Most occurring characters

ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter319
86.4%
Uppercase Letter50
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n45
14.1%
e44
13.8%
i42
13.2%
s37
11.6%
a32
10.0%
h24
7.5%
g21
6.6%
r17
 
5.3%
o16
 
5.0%
w12
 
3.8%
Other values (8)29
9.1%
Uppercase Letter
ValueCountFrequency (%)
C11
22.0%
N10
20.0%
E8
16.0%
R6
12.0%
S4
 
8.0%
K3
 
6.0%
J2
 
4.0%
A2
 
4.0%
D1
 
2.0%
T1
 
2.0%
Other values (2)2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin369
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
['Drama', 'Crime', 'Thriller']
10 
[]
['Comedy']
['Drama', 'Comedy', 'Romance']
['Drama', 'Romance']
Other values (16)
19 

Length

Max length43
Median length35
Mean length21
Min length2

Characters and Unicode

Total characters1050
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)26.0%

Sample

1st row['Children', 'Family']
2nd row[]
3rd row['Action', 'Anime', 'Science-Fiction']
4th row['Anime', 'Science-Fiction']
5th row['Action', 'Adventure', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
['Drama', 'Crime', 'Thriller']10
20.0%
[]9
18.0%
['Comedy']5
10.0%
['Drama', 'Comedy', 'Romance']4
 
8.0%
['Drama', 'Romance']3
 
6.0%
['Comedy', 'Adventure', 'Children']2
 
4.0%
['Drama', 'Action', 'Crime']2
 
4.0%
['Drama']2
 
4.0%
['Children', 'Family']1
 
2.0%
['Drama', 'Comedy', 'Crime']1
 
2.0%
Other values (11)11
22.0%

Length

2022-05-09T21:22:20.266583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama23
21.3%
crime14
13.0%
comedy13
12.0%
thriller11
10.2%
9
 
8.3%
romance8
 
7.4%
action5
 
4.6%
anime5
 
4.6%
children4
 
3.7%
adventure3
 
2.8%
Other values (9)13
12.0%

Most occurring characters

ValueCountFrequency (%)
'198
18.9%
r76
 
7.2%
e70
 
6.7%
m64
 
6.1%
a59
 
5.6%
,58
 
5.5%
58
 
5.5%
i52
 
5.0%
]50
 
4.8%
[50
 
4.8%
Other values (22)315
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter531
50.6%
Other Punctuation256
24.4%
Uppercase Letter102
 
9.7%
Space Separator58
 
5.5%
Close Punctuation50
 
4.8%
Open Punctuation50
 
4.8%
Dash Punctuation3
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r76
14.3%
e70
13.2%
m64
12.1%
a59
11.1%
i52
9.8%
o34
6.4%
n33
6.2%
l28
 
5.3%
c23
 
4.3%
y21
 
4.0%
Other values (7)71
13.4%
Uppercase Letter
ValueCountFrequency (%)
C31
30.4%
D23
22.5%
A13
12.7%
T11
 
10.8%
R8
 
7.8%
F5
 
4.9%
S5
 
4.9%
M3
 
2.9%
H3
 
2.9%
Other Punctuation
ValueCountFrequency (%)
'198
77.3%
,58
 
22.7%
Space Separator
ValueCountFrequency (%)
58
100.0%
Close Punctuation
ValueCountFrequency (%)
]50
100.0%
Open Punctuation
ValueCountFrequency (%)
[50
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin633
60.3%
Common417
39.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r76
12.0%
e70
11.1%
m64
 
10.1%
a59
 
9.3%
i52
 
8.2%
o34
 
5.4%
n33
 
5.2%
C31
 
4.9%
l28
 
4.4%
c23
 
3.6%
Other values (16)163
25.8%
Common
ValueCountFrequency (%)
'198
47.5%
,58
 
13.9%
58
 
13.9%
]50
 
12.0%
[50
 
12.0%
-3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'198
18.9%
r76
 
7.2%
e70
 
6.7%
m64
 
6.1%
a59
 
5.6%
,58
 
5.5%
58
 
5.5%
i52
 
5.0%
]50
 
4.8%
[50
 
4.8%
Other values (22)315
30.0%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Running
30 
Ended
18 
To Be Determined
 
2

Length

Max length16
Median length7
Mean length6.64
Min length5

Characters and Unicode

Total characters332
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowRunning
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running30
60.0%
Ended18
36.0%
To Be Determined2
 
4.0%

Length

2022-05-09T21:22:20.362348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:20.442386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running30
55.6%
ended18
33.3%
to2
 
3.7%
be2
 
3.7%
determined2
 
3.7%

Most occurring characters

ValueCountFrequency (%)
n110
33.1%
d38
 
11.4%
i32
 
9.6%
R30
 
9.0%
u30
 
9.0%
g30
 
9.0%
e26
 
7.8%
E18
 
5.4%
4
 
1.2%
T2
 
0.6%
Other values (6)12
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter274
82.5%
Uppercase Letter54
 
16.3%
Space Separator4
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n110
40.1%
d38
 
13.9%
i32
 
11.7%
u30
 
10.9%
g30
 
10.9%
e26
 
9.5%
o2
 
0.7%
t2
 
0.7%
r2
 
0.7%
m2
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
R30
55.6%
E18
33.3%
T2
 
3.7%
B2
 
3.7%
D2
 
3.7%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin328
98.8%
Common4
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n110
33.5%
d38
 
11.6%
i32
 
9.8%
R30
 
9.1%
u30
 
9.1%
g30
 
9.1%
e26
 
7.9%
E18
 
5.5%
T2
 
0.6%
o2
 
0.6%
Other values (5)10
 
3.0%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n110
33.1%
d38
 
11.4%
i32
 
9.6%
R30
 
9.0%
u30
 
9.0%
g30
 
9.0%
e26
 
7.8%
E18
 
5.4%
4
 
1.2%
T2
 
0.6%
Other values (6)12
 
3.6%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)50.0%
Missing18
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean36.25
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:20.521181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.55
Q115
median35
Q345
95-th percentile87
Maximum120
Range116
Interquartile range (IQR)30

Descriptive statistics

Standard deviation27.92732504
Coefficient of variation (CV)0.7704089666
Kurtosis3.403441749
Mean36.25
Median Absolute Deviation (MAD)15
Skewness1.633091421
Sum1160
Variance779.9354839
MonotonicityNot monotonic
2022-05-09T21:22:20.615073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
458
16.0%
153
 
6.0%
603
 
6.0%
1202
 
4.0%
122
 
4.0%
202
 
4.0%
252
 
4.0%
402
 
4.0%
71
 
2.0%
501
 
2.0%
Other values (6)6
 
12.0%
(Missing)18
36.0%
ValueCountFrequency (%)
41
 
2.0%
71
 
2.0%
81
 
2.0%
91
 
2.0%
111
 
2.0%
122
4.0%
153
6.0%
202
4.0%
221
 
2.0%
252
4.0%
ValueCountFrequency (%)
1202
 
4.0%
603
 
6.0%
501
 
2.0%
458
16.0%
402
 
4.0%
301
 
2.0%
252
 
4.0%
221
 
2.0%
202
 
4.0%
153
 
6.0%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)55.3%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean38
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:20.708826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q120
median43
Q345
95-th percentile65.4
Maximum120
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.12107143
Coefficient of variation (CV)0.6347650376
Kurtosis4.013355234
Mean38
Median Absolute Deviation (MAD)15
Skewness1.52983711
Sum1786
Variance581.826087
MonotonicityNot monotonic
2022-05-09T21:22:20.802725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
439
18.0%
457
14.0%
153
 
6.0%
202
 
4.0%
92
 
4.0%
602
 
4.0%
122
 
4.0%
1202
 
4.0%
321
 
2.0%
591
 
2.0%
Other values (16)16
32.0%
(Missing)3
 
6.0%
ValueCountFrequency (%)
41
 
2.0%
81
 
2.0%
92
4.0%
122
4.0%
131
 
2.0%
153
6.0%
161
 
2.0%
202
4.0%
211
 
2.0%
221
 
2.0%
ValueCountFrequency (%)
1202
 
4.0%
661
 
2.0%
641
 
2.0%
602
 
4.0%
591
 
2.0%
571
 
2.0%
471
 
2.0%
457
14.0%
439
18.0%
401
 
2.0%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27
12 
2020-11-22
2020-11-29
 
2
2018-07-13
 
2
2020-09-01
 
2
Other values (26)
29 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)46.0%

Sample

1st row1978-06-10
2nd row2015-11-27
3rd row2020-10-18
4th row2020-11-29
5th row2020-03-08

Common Values

ValueCountFrequency (%)
2020-12-2712
24.0%
2020-11-223
 
6.0%
2020-11-292
 
4.0%
2018-07-132
 
4.0%
2020-09-012
 
4.0%
2020-12-202
 
4.0%
2020-12-162
 
4.0%
2020-12-262
 
4.0%
2019-07-291
 
2.0%
2017-11-021
 
2.0%
Other values (21)21
42.0%

Length

2022-05-09T21:22:20.896790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2712
24.0%
2020-11-223
 
6.0%
2020-11-292
 
4.0%
2018-07-132
 
4.0%
2020-09-012
 
4.0%
2020-12-202
 
4.0%
2020-12-162
 
4.0%
2020-12-262
 
4.0%
2020-11-081
 
2.0%
2018-10-241
 
2.0%
Other values (21)21
42.0%

Most occurring characters

ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
80.0%
Dash Punctuation100
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2140
35.0%
0116
29.0%
175
18.8%
723
 
5.8%
913
 
3.2%
89
 
2.2%
68
 
2.0%
36
 
1.5%
55
 
1.2%
45
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
nan
32 
2020-12-27
10 
2021-01-25
 
2
2020-12-31
 
1
2021-01-17
 
1
Other values (4)

Length

Max length10
Median length3
Mean length5.52
Min length3

Characters and Unicode

Total characters276
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)12.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan32
64.0%
2020-12-2710
 
20.0%
2021-01-252
 
4.0%
2020-12-311
 
2.0%
2021-01-171
 
2.0%
2021-01-031
 
2.0%
2021-03-011
 
2.0%
2021-01-311
 
2.0%
2020-12-301
 
2.0%

Length

2022-05-09T21:22:20.975331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:21.100923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan32
64.0%
2020-12-2710
 
20.0%
2021-01-252
 
4.0%
2020-12-311
 
2.0%
2021-01-171
 
2.0%
2021-01-031
 
2.0%
2021-03-011
 
2.0%
2021-01-311
 
2.0%
2020-12-301
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n64
23.2%
260
21.7%
039
14.1%
-36
13.0%
a32
11.6%
127
9.8%
711
 
4.0%
35
 
1.8%
52
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number144
52.2%
Lowercase Letter96
34.8%
Dash Punctuation36
 
13.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
260
41.7%
039
27.1%
127
18.8%
711
 
7.6%
35
 
3.5%
52
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
n64
66.7%
a32
33.3%
Dash Punctuation
ValueCountFrequency (%)
-36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common180
65.2%
Latin96
34.8%

Most frequent character per script

Common
ValueCountFrequency (%)
260
33.3%
039
21.7%
-36
20.0%
127
15.0%
711
 
6.1%
35
 
2.8%
52
 
1.1%
Latin
ValueCountFrequency (%)
n64
66.7%
a32
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n64
23.2%
260
21.7%
039
14.1%
-36
13.0%
a32
11.6%
127
9.8%
711
 
4.0%
35
 
1.8%
52
 
0.7%

_embedded_show_officialSite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d
nan
https://disneynow.com/shows/fancy-nancy
 
2
https://www.iqiyi.com/a_19rrhllpip.html
 
2
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel
 
2
Other values (31)
31 

Length

Max length85
Median length72
Mean length49.52
Min length3

Characters and Unicode

Total characters2476
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)62.0%

Sample

1st rowhttps://okko.tv/serial/prostokvashino
2nd rowhttp://seasonvar.ru/serial-12772-TOP-10_po_versii_Seasonvarru-1-season.html
3rd rowhttps://v.qq.com/detail/q/q72jd29a3oxflsr.html
4th rowhttps://v.qq.com/detail/3/324olz7ilvo2j5f.html
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d8
 
16.0%
nan5
 
10.0%
https://disneynow.com/shows/fancy-nancy2
 
4.0%
https://www.iqiyi.com/a_19rrhllpip.html2
 
4.0%
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel2
 
4.0%
https://okko.tv/serial/prostokvashino1
 
2.0%
https://pro-tv.info/projects/dekonstruktsiya/1
 
2.0%
https://www.discoveryplus.se/program/pappas-pojkar1
 
2.0%
https://shahid.mbc.net/en/series/Lu'lu'/series-8256961
 
2.0%
https://www.netflix.com/title/814497541
 
2.0%
Other values (26)26
52.0%

Length

2022-05-09T21:22:21.214787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d8
 
16.0%
nan5
 
10.0%
https://disneynow.com/shows/fancy-nancy2
 
4.0%
https://www.iqiyi.com/a_19rrhllpip.html2
 
4.0%
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaobjecttype=channel2
 
4.0%
https://www.atresplayer.com/series/byanamilan1
 
2.0%
https://www.youtube.com/channel/uc1efxmjnkjitxpfwty6rswg1
 
2.0%
https://viaplay.dk/serier/friheden1
 
2.0%
https://www.youtube.com/user/scishow1
 
2.0%
https://tv.nrk.no/serie/labyrint1
 
2.0%
Other values (26)26
52.0%

Most occurring characters

ValueCountFrequency (%)
/208
 
8.4%
e168
 
6.8%
t165
 
6.7%
s136
 
5.5%
o121
 
4.9%
i98
 
4.0%
h97
 
3.9%
a97
 
3.9%
c96
 
3.9%
n94
 
3.8%
Other values (57)1196
48.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1719
69.4%
Other Punctuation336
 
13.6%
Decimal Number289
 
11.7%
Dash Punctuation80
 
3.2%
Uppercase Letter44
 
1.8%
Connector Punctuation6
 
0.2%
Math Symbol2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e168
 
9.8%
t165
 
9.6%
s136
 
7.9%
o121
 
7.0%
i98
 
5.7%
h97
 
5.6%
a97
 
5.6%
c96
 
5.6%
n94
 
5.5%
r83
 
4.8%
Other values (16)564
32.8%
Uppercase Letter
ValueCountFrequency (%)
T5
 
11.4%
C5
 
11.4%
O4
 
9.1%
F3
 
6.8%
U3
 
6.8%
A2
 
4.5%
W2
 
4.5%
B2
 
4.5%
Y2
 
4.5%
Z2
 
4.5%
Other values (12)14
31.8%
Decimal Number
ValueCountFrequency (%)
245
15.6%
037
12.8%
637
12.8%
831
10.7%
928
9.7%
428
9.7%
724
8.3%
123
8.0%
520
6.9%
316
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/208
61.9%
.78
 
23.2%
:45
 
13.4%
?2
 
0.6%
'2
 
0.6%
%1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-80
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Math Symbol
ValueCountFrequency (%)
=2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1763
71.2%
Common713
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e168
 
9.5%
t165
 
9.4%
s136
 
7.7%
o121
 
6.9%
i98
 
5.6%
h97
 
5.5%
a97
 
5.5%
c96
 
5.4%
n94
 
5.3%
r83
 
4.7%
Other values (38)608
34.5%
Common
ValueCountFrequency (%)
/208
29.2%
-80
 
11.2%
.78
 
10.9%
245
 
6.3%
:45
 
6.3%
037
 
5.2%
637
 
5.2%
831
 
4.3%
928
 
3.9%
428
 
3.9%
Other values (9)96
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/208
 
8.4%
e168
 
6.8%
t165
 
6.7%
s136
 
5.5%
o121
 
4.9%
i98
 
4.0%
h97
 
3.9%
a97
 
3.9%
c96
 
3.9%
n94
 
3.8%
Other values (57)1196
48.3%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.08
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:21.321418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q118
median31.5
Q372.25
95-th percentile76
Maximum88
Range87
Interquartile range (IQR)54.25

Descriptive statistics

Standard deviation27.15476431
Coefficient of variation (CV)0.6610215264
Kurtosis-1.446077151
Mean41.08
Median Absolute Deviation (MAD)20.5
Skewness0.2691134983
Sum2054
Variance737.3812245
MonotonicityNot monotonic
2022-05-09T21:22:21.526356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
758
 
16.0%
184
 
8.0%
273
 
6.0%
242
 
4.0%
282
 
4.0%
172
 
4.0%
702
 
4.0%
412
 
4.0%
342
 
4.0%
592
 
4.0%
Other values (19)21
42.0%
ValueCountFrequency (%)
11
2.0%
21
2.0%
31
2.0%
61
2.0%
81
2.0%
101
2.0%
121
2.0%
141
2.0%
151
2.0%
172
4.0%
ValueCountFrequency (%)
882
 
4.0%
762
 
4.0%
758
16.0%
731
 
2.0%
702
 
4.0%
671
 
2.0%
592
 
4.0%
571
 
2.0%
441
 
2.0%
412
 
4.0%

_embedded_show_webChannel
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)53.2%
Missing3
Missing (%)6.0%
Memory size528.0 B
{'id': 330, 'name': 'HBO Nordic', 'country': None, 'officialSite': None}
{'id': 21, 'name': 'YouTube', 'country': None, 'officialSite': 'https://www.youtube.com'}
{'id': 226, 'name': 'Mango TV', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://w.mgtv.com/'}
{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}
{'id': 67, 'name': 'iQIYI', 'country': None, 'officialSite': 'https://www.iq.com/'}
 
2
Other values (20)
24 

Length

Max length158
Median length141
Mean length110.7234043
Min length67

Characters and Unicode

Total characters5204
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)34.0%

Sample

1st row{'id': 56, 'name': 'Seasonvar', 'country': {'name': 'Russian Federation', 'code': 'RU', 'timezone': 'Asia/Kamchatka'}, 'officialSite': None}
2nd row{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}
3rd row{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}
4th row{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}
5th row{'id': 51, 'name': 'Bilibili', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': None}

Common Values

ValueCountFrequency (%)
{'id': 330, 'name': 'HBO Nordic', 'country': None, 'officialSite': None}8
16.0%
{'id': 21, 'name': 'YouTube', 'country': None, 'officialSite': 'https://www.youtube.com'}6
 
12.0%
{'id': 226, 'name': 'Mango TV', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://w.mgtv.com/'}4
 
8.0%
{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}3
 
6.0%
{'id': 67, 'name': 'iQIYI', 'country': None, 'officialSite': 'https://www.iq.com/'}2
 
4.0%
{'id': 83, 'name': 'DisneyNOW', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}2
 
4.0%
{'id': 294, 'name': 'Kakao TV', 'country': {'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}, 'officialSite': 'https://tv.kakao.com/top'}2
 
4.0%
{'id': 377, 'name': 'ATRESplayer PREMIUM', 'country': {'name': 'Spain', 'code': 'ES', 'timezone': 'Europe/Madrid'}, 'officialSite': None}2
 
4.0%
{'id': 379, 'name': 'Shahid', 'country': None, 'officialSite': None}2
 
4.0%
{'id': 265, 'name': 'ESPN+', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}1
 
2.0%
Other values (15)15
30.0%
(Missing)3
 
6.0%

Length

2022-05-09T21:22:21.624956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
name71
 
13.2%
none48
 
8.9%
id47
 
8.7%
country47
 
8.7%
officialsite47
 
8.7%
code24
 
4.5%
timezone24
 
4.5%
hbo9
 
1.7%
tv9
 
1.7%
china9
 
1.7%
Other values (93)203
37.7%

Most occurring characters

ValueCountFrequency (%)
'802
15.4%
491
 
9.4%
e313
 
6.0%
i296
 
5.7%
:282
 
5.4%
o280
 
5.4%
n237
 
4.6%
a219
 
4.2%
t208
 
4.0%
,193
 
3.7%
Other values (58)1883
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2653
51.0%
Other Punctuation1403
27.0%
Space Separator491
 
9.4%
Uppercase Letter381
 
7.3%
Decimal Number125
 
2.4%
Open Punctuation71
 
1.4%
Close Punctuation71
 
1.4%
Connector Punctuation6
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e313
11.8%
i296
11.2%
o280
10.6%
n237
 
8.9%
a219
 
8.3%
t208
 
7.8%
c165
 
6.2%
m131
 
4.9%
f100
 
3.8%
d94
 
3.5%
Other values (15)610
23.0%
Uppercase Letter
ValueCountFrequency (%)
N81
21.3%
S80
21.0%
T27
 
7.1%
A22
 
5.8%
C19
 
5.0%
U15
 
3.9%
R15
 
3.9%
O15
 
3.9%
Y14
 
3.7%
B12
 
3.1%
Other values (13)81
21.3%
Decimal Number
ValueCountFrequency (%)
331
24.8%
224
19.2%
118
14.4%
015
12.0%
711
 
8.8%
68
 
6.4%
47
 
5.6%
94
 
3.2%
54
 
3.2%
83
 
2.4%
Other Punctuation
ValueCountFrequency (%)
'802
57.2%
:282
 
20.1%
,193
 
13.8%
/83
 
5.9%
.43
 
3.1%
Space Separator
ValueCountFrequency (%)
491
100.0%
Open Punctuation
ValueCountFrequency (%)
{71
100.0%
Close Punctuation
ValueCountFrequency (%)
}71
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Math Symbol
ValueCountFrequency (%)
+3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3034
58.3%
Common2170
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e313
 
10.3%
i296
 
9.8%
o280
 
9.2%
n237
 
7.8%
a219
 
7.2%
t208
 
6.9%
c165
 
5.4%
m131
 
4.3%
f100
 
3.3%
d94
 
3.1%
Other values (38)991
32.7%
Common
ValueCountFrequency (%)
'802
37.0%
491
22.6%
:282
 
13.0%
,193
 
8.9%
/83
 
3.8%
{71
 
3.3%
}71
 
3.3%
.43
 
2.0%
331
 
1.4%
224
 
1.1%
Other values (10)79
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'802
15.4%
491
 
9.4%
e313
 
6.0%
i296
 
5.7%
:282
 
5.4%
o280
 
5.4%
n237
 
4.6%
a219
 
4.2%
t208
 
4.0%
,193
 
3.7%
Other values (58)1883
36.2%

_embedded_show_dvdCountry
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
nan
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan50
100.0%

Length

2022-05-09T21:22:21.703350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:22:21.781460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan50
100.0%

Most occurring characters

ValueCountFrequency (%)
n100
66.7%
a50
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter150
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n100
66.7%
a50
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin150
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n100
66.7%
a50
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n100
66.7%
a50
33.3%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
<p>In the middle of the night, far north in Norway at Finnmarksvidda, the Sami teenager Elle Jannok is on her way home from a party, when she finds a cell phone ringing. The phone belongs to a missing girl, Sofie. A couple of days later, the local police discover Sofie's body in a caravan three hours south of Finnmark.</p><p>Maja Angell, who is studying at the University of London, is defending her highly debated doctoral thesis on criminology &amp; profiling as she hears about the murder case in her old home town in Northern Norway. She defies all warnings and leaves the University to travel North. She has a message for the local police: the man charged with the murder, is not the killer. But no one's willing to listen to her theories. Not until Maja dares to delve into her own most dangerous, repressed memories of childhood will she be able to stop him and soon she realize that the killer is closer than she ever imagined.</p>
nan
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>
 
2
<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>
 
2
<p>Ju Xuanwen (Wan Yan Lo-yun) is a man with a noble appearance and many virtues. It is a pity that he fell ill with neurosis at a young age - after an unexplained car accident he falls into a delusional state and considers himself a prince. Since then, he no longer cares about the activities of his company and concentrates on becoming emperor.<br />Lo Huai (Chuang Da Fei) - psychiatrist on the verge of bankruptcy. Because of the need for money, she took responsibility for the treatment of Ju Xuanwen. However, she did not expect her peaceful days to end one day. Spending time together, they began to fall in love with each other.</p>
 
2
Other values (28)
30 

Length

Max length941
Median length621
Mean length434.82
Min length3

Characters and Unicode

Total characters21741
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)52.0%

Sample

1st rownan
2nd rownan
3rd row<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>
4th row<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>
5th row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>

Common Values

ValueCountFrequency (%)
<p>In the middle of the night, far north in Norway at Finnmarksvidda, the Sami teenager Elle Jannok is on her way home from a party, when she finds a cell phone ringing. The phone belongs to a missing girl, Sofie. A couple of days later, the local police discover Sofie's body in a caravan three hours south of Finnmark.</p><p>Maja Angell, who is studying at the University of London, is defending her highly debated doctoral thesis on criminology &amp; profiling as she hears about the murder case in her old home town in Northern Norway. She defies all warnings and leaves the University to travel North. She has a message for the local police: the man charged with the murder, is not the killer. But no one's willing to listen to her theories. Not until Maja dares to delve into her own most dangerous, repressed memories of childhood will she be able to stop him and soon she realize that the killer is closer than she ever imagined.</p>8
 
16.0%
nan6
 
12.0%
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>2
 
4.0%
<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>2
 
4.0%
<p>Ju Xuanwen (Wan Yan Lo-yun) is a man with a noble appearance and many virtues. It is a pity that he fell ill with neurosis at a young age - after an unexplained car accident he falls into a delusional state and considers himself a prince. Since then, he no longer cares about the activities of his company and concentrates on becoming emperor.<br />Lo Huai (Chuang Da Fei) - psychiatrist on the verge of bankruptcy. Because of the need for money, she took responsibility for the treatment of Ju Xuanwen. However, she did not expect her peaceful days to end one day. Spending time together, they began to fall in love with each other.</p>2
 
4.0%
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>2
 
4.0%
<p>Ke Ying is a talented economics lecturer who is forced to help psychopath Feng Xiao Sheng gain real power within his corporation. The street smart Xiao Wu is a police informant, and when he discovers Fu's company is laundering money with foreign bank accounts, he uses his position as Feng Xiao Sheng's right-hand man to collect evidence. He befriends Ke Ying, and the two work together to destroy the criminal organization.</p>2
 
4.0%
<p><b>Peyton's Places</b> offers a fun, insightful tour through 100 years of football, following the sport and the league's rise to an American cultural touchstone. For nearly a year, Manning has crisscrossed the country, visiting the people and places that have played an important part in the making of the NFL—highlighting memorable events, teams, players, and trends over the past century.</p>1
 
2.0%
<p>A group of aspiring idols gather at Takanashi Productions and are entrusted with the company's future. The seven men who have just met represent a variety of totally different personalities. However, they each have their own charm and possess unknown potential as idols. Forming a group, they take their first step together as <b>IDOLiSH7</b>. Their brilliantly shining dancing forms onstage eventually begin captivating the hearts of the people. In the glorious but sometimes harsh world of idols, they aim for the top with dreams in their hearts!</p>1
 
2.0%
<p>The lads and lasses of Achievement Hunter congregate each week to discuss the important questions in life. Plus drink beer.</p>1
 
2.0%
Other values (23)23
46.0%

Length

2022-05-09T21:22:21.875200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the254
 
6.9%
to120
 
3.2%
of116
 
3.1%
a114
 
3.1%
and97
 
2.6%
is73
 
2.0%
in70
 
1.9%
she67
 
1.8%
her57
 
1.5%
his34
 
0.9%
Other values (1004)2704
73.0%

Most occurring characters

ValueCountFrequency (%)
3644
16.8%
e2080
 
9.6%
t1370
 
6.3%
o1337
 
6.1%
a1327
 
6.1%
n1291
 
5.9%
i1224
 
5.6%
r1072
 
4.9%
s1059
 
4.9%
h976
 
4.5%
Other values (69)6361
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16612
76.4%
Space Separator3657
 
16.8%
Other Punctuation553
 
2.5%
Uppercase Letter552
 
2.5%
Math Symbol304
 
1.4%
Dash Punctuation34
 
0.2%
Decimal Number17
 
0.1%
Close Punctuation6
 
< 0.1%
Open Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2080
12.5%
t1370
 
8.2%
o1337
 
8.0%
a1327
 
8.0%
n1291
 
7.8%
i1224
 
7.4%
r1072
 
6.5%
s1059
 
6.4%
h976
 
5.9%
l734
 
4.4%
Other values (16)4142
24.9%
Uppercase Letter
ValueCountFrequency (%)
S69
 
12.5%
N55
 
10.0%
T48
 
8.7%
F42
 
7.6%
A34
 
6.2%
L28
 
5.1%
M26
 
4.7%
H26
 
4.7%
U25
 
4.5%
J24
 
4.3%
Other values (16)175
31.7%
Other Punctuation
ValueCountFrequency (%)
,203
36.7%
.170
30.7%
/80
 
14.5%
'44
 
8.0%
"15
 
2.7%
!12
 
2.2%
:11
 
2.0%
&8
 
1.4%
;8
 
1.4%
?2
 
0.4%
Decimal Number
ValueCountFrequency (%)
06
35.3%
14
23.5%
52
 
11.8%
41
 
5.9%
71
 
5.9%
81
 
5.9%
21
 
5.9%
31
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-25
73.5%
8
 
23.5%
1
 
2.9%
Space Separator
ValueCountFrequency (%)
3644
99.6%
 13
 
0.4%
Math Symbol
ValueCountFrequency (%)
<152
50.0%
>152
50.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17164
78.9%
Common4577
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2080
12.1%
t1370
 
8.0%
o1337
 
7.8%
a1327
 
7.7%
n1291
 
7.5%
i1224
 
7.1%
r1072
 
6.2%
s1059
 
6.2%
h976
 
5.7%
l734
 
4.3%
Other values (42)4694
27.3%
Common
ValueCountFrequency (%)
3644
79.6%
,203
 
4.4%
.170
 
3.7%
<152
 
3.3%
>152
 
3.3%
/80
 
1.7%
'44
 
1.0%
-25
 
0.5%
"15
 
0.3%
 13
 
0.3%
Other values (17)79
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII21719
99.9%
None13
 
0.1%
Punctuation9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3644
16.8%
e2080
 
9.6%
t1370
 
6.3%
o1337
 
6.2%
a1327
 
6.1%
n1291
 
5.9%
i1224
 
5.6%
r1072
 
4.9%
s1059
 
4.9%
h976
 
4.5%
Other values (66)6339
29.2%
None
ValueCountFrequency (%)
 13
100.0%
Punctuation
ValueCountFrequency (%)
8
88.9%
1
 
11.1%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1630805527
Minimum1603467037
Maximum1651749165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T21:22:21.985157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1603467037
5-th percentile1609699187
Q11614642259
median1627558682
Q31648361945
95-th percentile1651210558
Maximum1651749165
Range48282128
Interquartile range (IQR)33719686

Descriptive statistics

Standard deviation16777222.93
Coefficient of variation (CV)0.01028769075
Kurtosis-1.748433454
Mean1630805527
Median Absolute Deviation (MAD)16985825
Skewness0.005456348607
Sum8.154027633 × 1010
Variance2.814752092 × 1014
MonotonicityNot monotonic
2022-05-09T21:22:22.079279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
16146422598
 
16.0%
16349480182
 
4.0%
16243853322
 
4.0%
16097998962
 
4.0%
16154510692
 
4.0%
16096167882
 
4.0%
16513386481
 
2.0%
16470729551
 
2.0%
16457532861
 
2.0%
16443270091
 
2.0%
Other values (28)28
56.0%
ValueCountFrequency (%)
16034670371
 
2.0%
16096167882
 
4.0%
16097998962
 
4.0%
16110394971
 
2.0%
16115079791
 
2.0%
16120609221
 
2.0%
16129809601
 
2.0%
16146422598
16.0%
16154510692
 
4.0%
16215672851
 
2.0%
ValueCountFrequency (%)
16517491651
2.0%
16513386481
2.0%
16512343741
2.0%
16511814491
2.0%
16509491161
2.0%
16509088001
2.0%
16500542891
2.0%
16499562021
2.0%
16495725941
2.0%
16494234441
2.0%

_links_self_href
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2087588
 
1
https://api.tvmaze.com/episodes/2065447
 
1
https://api.tvmaze.com/episodes/2090654
 
1
https://api.tvmaze.com/episodes/2090655
 
1
Other values (45)
45 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters1950
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
2.0%
https://api.tvmaze.com/episodes/20875881
 
2.0%
https://api.tvmaze.com/episodes/20654471
 
2.0%
https://api.tvmaze.com/episodes/20906541
 
2.0%
https://api.tvmaze.com/episodes/20906551
 
2.0%
https://api.tvmaze.com/episodes/21692031
 
2.0%
https://api.tvmaze.com/episodes/23122231
 
2.0%
https://api.tvmaze.com/episodes/23122241
 
2.0%
https://api.tvmaze.com/episodes/23122251
 
2.0%
https://api.tvmaze.com/episodes/23122261
 
2.0%
Other values (40)40
80.0%

Length

2022-05-09T21:22:22.197367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
2.0%
https://api.tvmaze.com/episodes/23244301
 
2.0%
https://api.tvmaze.com/episodes/19986151
 
2.0%
https://api.tvmaze.com/episodes/19640001
 
2.0%
https://api.tvmaze.com/episodes/19954051
 
2.0%
https://api.tvmaze.com/episodes/20077601
 
2.0%
https://api.tvmaze.com/episodes/19857891
 
2.0%
https://api.tvmaze.com/episodes/20396221
 
2.0%
https://api.tvmaze.com/episodes/20396231
 
2.0%
https://api.tvmaze.com/episodes/23244271
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
/200
 
10.3%
t150
 
7.7%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
o100
 
5.1%
Other values (16)650
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1250
64.1%
Other Punctuation350
 
17.9%
Decimal Number350
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t150
12.0%
p150
12.0%
s150
12.0%
e150
12.0%
a100
8.0%
i100
8.0%
m100
8.0%
o100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%
Decimal Number
ValueCountFrequency (%)
277
22.0%
946
13.1%
041
11.7%
140
11.4%
330
 
8.6%
426
 
7.4%
825
 
7.1%
723
 
6.6%
521
 
6.0%
621
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/200
57.1%
.100
28.6%
:50
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1250
64.1%
Common700
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/200
28.6%
.100
14.3%
277
 
11.0%
:50
 
7.1%
946
 
6.6%
041
 
5.9%
140
 
5.7%
330
 
4.3%
426
 
3.7%
825
 
3.6%
Other values (3)65
 
9.3%
Latin
ValueCountFrequency (%)
t150
12.0%
p150
12.0%
s150
12.0%
e150
12.0%
a100
8.0%
i100
8.0%
m100
8.0%
o100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/200
 
10.3%
t150
 
7.7%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
o100
 
5.1%
Other values (16)650
33.3%

Interactions

2022-05-09T21:22:14.804230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:03.338843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:05.833732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.256667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.299130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.417916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.305596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.401704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.449558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.255487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:04.050606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.427885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.456049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.505790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.963458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.585067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.608235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.889523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.353091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:04.332386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.529585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.565695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.598565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.138558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.688212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.710715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.987241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.447830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:04.517256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.628267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.671845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.805845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.300508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.773219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.804645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.079223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.538442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:04.691897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.716365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.758909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.891160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.453053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.860415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.903399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.259486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.724916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:05.014012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.881341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.927161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.055630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.694715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.001130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.061521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.430357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.816362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:05.160895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:06.975216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.021842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.141287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.831571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.097485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.155978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.535615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:15.907626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:05.360119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.070592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.120918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.235990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:10.975012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.209992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.244041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.633197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:16.007379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:05.602251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:07.167904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:08.208422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:09.324851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:11.150599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:12.308480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:13.343012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:22:14.715612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:22:22.269952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:22:22.395564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:22:22.548844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:22:22.701145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:22:22.920922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:22:16.214216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:22:16.949315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:22:17.161950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:22:17.408380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_webChannel_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01993496https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-mehБобровый мех2.041.0regular2020-12-27nan2020-12-27T00:00:00+00:0019.0nan10892https://www.tvmaze.com/shows/10892/troe-iz-prostokvasinoТрое из ПростоквашиноAnimationRussian['Children', 'Family']Running7.015.01978-06-10nanhttps://okko.tv/serial/prostokvashino41.0Nonenannan1.651339e+09https://api.tvmaze.com/episodes/1977902
11993442https://www.tvmaze.com/episodes/1993442/top-10-po-versii-seasonvarru-2x12-top-10-samyh-ozidaemyh-novinok-v-mire-serialovТОП-10 самых ожидаемых новинок в мире сериалов2.012.0regular2020-12-27nan2020-12-27T00:00:00+00:007.0nan19628https://www.tvmaze.com/shows/19628/top-10-po-versii-seasonvarruТОП-10 по версии Seasonvar.ruTalk ShowRussian[]Running9.09.02015-11-27nanhttp://seasonvar.ru/serial-12772-TOP-10_po_versii_Seasonvarru-1-season.html28.0{'id': 56, 'name': 'Seasonvar', 'country': {'name': 'Russian Federation', 'code': 'RU', 'timezone': 'Asia/Kamchatka'}, 'officialSite': None}nannan1.651181e+09https://api.tvmaze.com/episodes/2015818
21956341https://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-12Episode 121.012.0regular2020-12-2710:002020-12-27T02:00:00+00:0015.0nan51471https://www.tvmaze.com/shows/51471/hero-returnHero ReturnAnimationChinese['Action', 'Anime', 'Science-Fiction']Running15.016.02020-10-18nanhttps://v.qq.com/detail/q/q72jd29a3oxflsr.html76.0{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}nan<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>1.603467e+09https://api.tvmaze.com/episodes/1964000
31988864https://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-6Episode 61.06.0regular2020-12-2710:002020-12-27T02:00:00+00:0021.0nan52178https://www.tvmaze.com/shows/52178/swallowed-starSwallowed StarAnimationChinese['Anime', 'Science-Fiction']RunningNaN21.02020-11-29nanhttps://v.qq.com/detail/3/324olz7ilvo2j5f.html88.0{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}nan<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>1.648371e+09https://api.tvmaze.com/episodes/1995405
42052512https://www.tvmaze.com/episodes/2052512/wu-shen-zhu-zai-1x87-episode-87Episode 871.087.0regular2020-12-2710:002020-12-27T02:00:00+00:008.0nan54033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running8.08.02020-03-08nanhttps://v.qq.com/detail/m/7q544xyrava3vxf.html76.0{'id': 104, 'name': 'Tencent QQ', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': 'https://v.qq.com/'}nan<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>1.649423e+09https://api.tvmaze.com/episodes/2007760
52012323https://www.tvmaze.com/episodes/2012323/mans-diary-2x08-episode-8Episode 82.08.0regular2020-12-27nan2020-12-27T04:00:00+00:0012.0nan50398https://www.tvmaze.com/shows/50398/mans-diaryMan's DiaryAnimationChinese['Anime', 'Supernatural']Running12.012.02019-07-21nanhttps://www.bilibili.com/bangumi/media/md43146223.0{'id': 51, 'name': 'Bilibili', 'country': {'name': 'China', 'code': 'CN', 'timezone': 'Asia/Shanghai'}, 'officialSite': None}nan<p>In the twenty-first century, gods and demons can no longer maintain balance due to the rapid development of human society. In an effort to restore proper order, the gods began to take care of saving the world, for which they sent a group of gods and monsters to the world of people, who must find there the " key " to salvation. Su moting is a girl with the personality of "demon child". When her parents asked her to leave home so that she could become independent and independent, she met the beautiful and charming God of Tianjin and the mysterious demon cat. So begins a new turbulent round of su moting's life.</p><p><br /> </p>1.611039e+09https://api.tvmaze.com/episodes/1985789
62005757https://www.tvmaze.com/episodes/2005757/legend-of-yun-qian-1x10-episode-10Episode 101.010.0regular2020-12-27nan2020-12-27T04:00:00+00:004.0nan52898https://www.tvmaze.com/shows/52898/legend-of-yun-qianLegend of Yun QianScriptedChinese['Drama', 'Romance', 'History']Ended4.04.02020-12-212020-12-31nan67.0{'id': 445, 'name': 'CTI TV', 'country': {'name': 'Taiwan, Province of China', 'code': 'TW', 'timezone': 'Asia/Taipei'}, 'officialSite': None}nan<p>The disciples of the Lingchuan Sect have guarded the Fans of Heaven and Earth for nearly a century. Mu Yun and Hua Yue are the only disciples of the sect that are left. The stubborn and disobedient Hua Yue unintentionally discovers that the Fan of Heaven possesses the power to travel through time. To escape being forced to study and practice martial arts by Mu Yun, Hua Yue travels to the future to have fun. Hundreds of years in the future she meets Xiao Qian who looks exactly like her. Secrets come to the surface, and adventures take place.</p>1.649956e+09https://api.tvmaze.com/episodes/2039622
71974055https://www.tvmaze.com/episodes/1974055/love-revolution-1x29-episode-29Episode 291.029.0regular2020-12-2717:002020-12-27T08:00:00+00:0020.0nan49948https://www.tvmaze.com/shows/49948/love-revolutionLove RevolutionScriptedKorean['Drama', 'Comedy', 'Romance']Ended20.020.02020-09-012020-12-27https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel34.0{'id': 294, 'name': 'Kakao TV', 'country': {'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}, 'officialSite': 'https://tv.kakao.com/top'}nan<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>1.609617e+09https://api.tvmaze.com/episodes/2039623
81974056https://www.tvmaze.com/episodes/1974056/love-revolution-1x30-episode-30Episode 301.030.0regular2020-12-2717:002020-12-27T08:00:00+00:0020.0nan49948https://www.tvmaze.com/shows/49948/love-revolutionLove RevolutionScriptedKorean['Drama', 'Comedy', 'Romance']Ended20.020.02020-09-012020-12-27https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel34.0{'id': 294, 'name': 'Kakao TV', 'country': {'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}, 'officialSite': 'https://tv.kakao.com/top'}nan<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>1.609617e+09https://api.tvmaze.com/episodes/2324427
92015367https://www.tvmaze.com/episodes/2015367/youtuber-class-1x01-episode-1Episode 11.01.0regular2020-12-2718:002020-12-27T09:00:00+00:0011.0nan53094https://www.tvmaze.com/shows/53094/youtuber-classYoutuber ClassScriptedKorean['Drama', 'Romance']Ended11.013.02020-12-272021-01-17https://m.tv.naver.com/whynottv/home24.0{'id': 30, 'name': 'Naver TVCast', 'country': {'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}, 'officialSite': 'https://tv.naver.com/'}nannan1.611508e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_webChannel_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
402267318https://www.tvmaze.com/episodes/2267318/dekonstrukcia-s01-special-postdekonstrukcia-s-vladimirom-surdinym-film-interstellarПостдеконструкция с Владимиром Сурдиным. Фильм "Интерстеллар"1.0NaNsignificant_special2020-12-27nan2020-12-27T12:00:00+00:0030.0nan60246https://www.tvmaze.com/shows/60246/dekonstrukciaДеконструкцияTalk ShowRussian[]Running60.060.02019-07-24nanhttps://pro-tv.info/projects/dekonstruktsiya/1.0{'id': 21, 'name': 'YouTube', 'country': None, 'officialSite': 'https://www.youtube.com'}nan<p>An educational online project of the Prodvizhenie TV channel and the Kino-Teatr.Ru website about how the past is depicted in real cinema and what place a work of art occupied in the context of its time. From classics to modern cinema, from epic films to TV series. The experts of "Deconstruction" - historians, reenactors, writers and journalists - will take each film apart and leave no stone unturned to find historical truth and fiction</p>1.650054e+09https://api.tvmaze.com/episodes/2008312
412318113https://www.tvmaze.com/episodes/2318113/bride-of-beirut-2x57-episode-57Episode 572.057.0regular2020-12-27nan2020-12-27T12:00:00+00:0045.0nan61755https://www.tvmaze.com/shows/61755/bride-of-beirutBride of BeirutScriptedArabic['Drama', 'Romance']Running45.0NaN2019-09-01nanhttps://shahid.mbc.net/en/series/Arous%20Beirut-season-1/season-376514-3765156.0{'id': 379, 'name': 'Shahid', 'country': None, 'officialSite': None}nan<p>Living with her aunt, kind-hearted Thourayya leads a simple life, but her world is upended when she crosses paths with the handsome and ambitious Fares.</p>1.650909e+09https://api.tvmaze.com/episodes/2015837
421990363https://www.tvmaze.com/episodes/1990363/fantastico-48x52-edition-of-12272020Edition of 12/27/202048.052.0regular2020-12-27nan2020-12-27T14:00:00+00:00NaNnan36907https://www.tvmaze.com/shows/36907/fantasticoFantásticoNewsPortuguese[]RunningNaNNaN1973-08-05nanhttp://g1.globo.com/fantastico/12.0{'id': 131, 'name': 'Globoplay', 'country': {'name': 'Brazil', 'code': 'BR', 'timezone': 'America/Noronha'}, 'officialSite': None}nannan1.625403e+09https://api.tvmaze.com/episodes/1996819
432126228https://www.tvmaze.com/episodes/2126228/this-week-in-tech-2020-12-27-the-best-of-twit-2020The Best of TWiT 20202020.052.0regular2020-12-27nan2020-12-27T17:00:00+00:00120.0nan17584https://www.tvmaze.com/shows/17584/this-week-in-techThis Week in TechNewsEnglish[]Running120.0120.02005-04-17nanhttps://twit.tv/shows/this-week-in-tech27.0{'id': 102, 'name': 'Twit', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p>Your first podcast of the week is the last word in tech. Join the top tech pundits in a roundtable discussion of the latest trends in high tech. Hosted by Leo Laporte and friends.</p>1.648029e+09https://api.tvmaze.com/episodes/2037724
442165932https://www.tvmaze.com/episodes/2165932/off-topic-2020-12-27-let-me-buy-your-games-263Let Me Buy Your Games! - #2632020.049.0regular2020-12-27nan2020-12-27T17:00:00+00:00120.0nan18752https://www.tvmaze.com/shows/18752/off-topicOff TopicTalk ShowEnglish['Comedy']Running120.0120.02015-12-06nanhttps://roosterteeth.com/series/off-topic-the-achievement-hunter-podcast39.0{'id': 32, 'name': 'Rooster Teeth', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p>The lads and lasses of Achievement Hunter congregate each week to discuss the important questions in life. Plus drink beer.</p>1.631188e+09https://api.tvmaze.com/episodes/2037725
451993462https://www.tvmaze.com/episodes/1993462/idolish7-2x15-discover-the-futureDiSCOVER THE FUTURE2.015.0regular2020-12-27nan2020-12-27T17:00:00+00:0025.0<p>Re:vale prepare to close out the inaugural concert. The IDOLiSH7 and Trigger shuffle units perform the opening act. While watching their juniors perform, Momo and Yuki look back on the past five years they've walked together. </p>33463https://www.tvmaze.com/shows/33463/idolish7IDOLiSH7AnimationJapanese['Anime', 'Music']Running25.025.02017-11-02nanhttp://idolish7.com/aninana/29.0Nonenan<p>A group of aspiring idols gather at Takanashi Productions and are entrusted with the company's future. The seven men who have just met represent a variety of totally different personalities. However, they each have their own charm and possess unknown potential as idols. Forming a group, they take their first step together as <b>IDOLiSH7</b>. Their brilliantly shining dancing forms onstage eventually begin captivating the hearts of the people. In the glorious but sometimes harsh world of idols, they aim for the top with dreams in their hearts!</p>1.628688e+09https://api.tvmaze.com/episodes/1988424
461975189https://www.tvmaze.com/episodes/1975189/fancy-nancy-2x37-new-years-nancyNew Year's Nancy2.037.0regular2020-12-2712:002020-12-27T17:00:00+00:0015.0<p>Nancy wants to stay up until midnight on New Year's Eve but finds that staying awake is harder than she thought.</p>34940https://www.tvmaze.com/shows/34940/fancy-nancyFancy NancyAnimationEnglish['Comedy', 'Adventure', 'Children']Running15.015.02018-07-13nanhttps://disneynow.com/shows/fancy-nancy59.0{'id': 83, 'name': 'DisneyNOW', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>1.634948e+09https://api.tvmaze.com/episodes/1993940
471981602https://www.tvmaze.com/episodes/1981602/peytons-places-2x05-terrell-owensTerrell Owens2.05.0regular2020-12-27nan2020-12-27T17:00:00+00:0030.0nan43207https://www.tvmaze.com/shows/43207/peytons-placesPeyton's PlacesDocumentaryEnglish['History', 'Sports']Running30.029.02019-07-29nanhttp://www.espn.com/watch/series/2043dd20-9cc0-4abe-b652-c8e7dfdfefa0/peyton-s-places41.0{'id': 265, 'name': 'ESPN+', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p><b>Peyton's Places</b> offers a fun, insightful tour through 100 years of football, following the sport and the league's rise to an American cultural touchstone. For nearly a year, Manning has crisscrossed the country, visiting the people and places that have played an important part in the making of the NFL—highlighting memorable events, teams, players, and trends over the past century.</p>1.647692e+09https://api.tvmaze.com/episodes/2037532
481975190https://www.tvmaze.com/episodes/1975190/fancy-nancy-2x38-nancys-gift-to-grandpaNancy's Gift to Grandpa2.038.0regular2020-12-2712:152020-12-27T17:15:00+00:0015.0<p>To cheer up Grandpa on a wintry day, Nancy draws a masterpiece in chalk on the driveway.</p>34940https://www.tvmaze.com/shows/34940/fancy-nancyFancy NancyAnimationEnglish['Comedy', 'Adventure', 'Children']Running15.015.02018-07-13nanhttps://disneynow.com/shows/fancy-nancy59.0{'id': 83, 'name': 'DisneyNOW', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>1.634948e+09https://api.tvmaze.com/episodes/2068252
492234691https://www.tvmaze.com/episodes/2234691/one-mo-chance-1x11-one-mo-finaleOne Mo' Finale1.011.0regular2020-12-2720:002020-12-28T01:00:00+00:0050.0<p>Weeks of laughter, fighting and love have finally led to this. Will Yodela receive "One Mo' Chance", or will it be Yummy?</p>59398https://www.tvmaze.com/shows/59398/one-mo-chanceOne Mo' ChanceRealityEnglish[]RunningNaN47.02020-10-11nanhttps://www.thezeusnetwork.com/one-mo-chance57.0{'id': 331, 'name': 'Zeus', 'country': {'name': 'United States', 'code': 'US', 'timezone': 'America/New_York'}, 'officialSite': None}nan<p>From the breakdown of his relationship with the mother of his children, to the death of his brother and partner Real," the last few years have been personally tough for Kamal Chance Givens. However, the original Stallionaire is now ready to get back on his horse to give love another shot. During Chance return to reality television we'll watch as he goes it alone to find the true love of his life in this new dating competition series.</p>1.649331e+09https://api.tvmaze.com/episodes/1996820